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In this paper, firstly, on the basis of time series analysis, considering the validity of data research data, it is necessary to carry out the pre-processing process of independent variables and dependent variables of temple frescoes and then select the characteristic variables of temple frescoes, and get the multivariate time series of temple frescoes through wavelet correlation analysis of the characteristic variables of temple frescoes. Then, construct the classification model of temple frescoes based on the multivariate time series through the image processing of temple frescoes, get the feature values applicable to the model classification, considering the accuracy and convergence of the model classification, the temple frescoes features are needed to be extracted and trained, and at the same time, the evolution of the historical style of the traditional temple frescoes is analyzed by examples. The results show that in terms of performance, the method of this paper achieves 90.34% accuracy, and the comparison results of two indicators, recall and F1 value, also show that this paper shows good performance compared with the other four models. In the correlation analysis, it is found that there is a significant correlation between the rich artistic heritage of dynasties (P=0.66>0.05), the economic prosperity of dynasties (P=0.75>0.05) and the evolution of the historical style of temple frescoes, and it is proposed that traditional temple frescoes are protected and developed paths.

Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects. [ABSTRACT FROM AUTHOR]

Curvilinear, multiblock summation-by-parts finite difference operators with the simultaneous approximation term method provide a stable and accurate framework for solving the wave equation in second order form. That said, the standard method can become arbitrarily stiff when characteristic boundary conditions and nonlinear interface conditions are used. Here we propose a new technique that avoids this stiffness by using characteristic variables to “upwind” the boundary and interface treatment. This is done through the introduction of an additional block boundary displacement variable. Using a unified energy, which expresses both the standard as well as characteristic boundary and interface treatment, we show that the resulting scheme has semidiscrete energy stability for the scalar anisotropic wave equation. The theoretical stability results are confirmed with numerical experiments that also demonstrate the accuracy and robustness of the proposed scheme. The numerical results also show that the characteristic scheme has a time step restriction based on standard wave propagation considerations and not the boundary closure. [ABSTRACT FROM AUTHOR]

With the rapid depletion of mineral resources, deep prospecting is becoming a frontier field in international geological exploration. The prediction of deep mineral resources is the premise and foundation of deep prospecting. However, conventional metallogenic predictive methods, which are mainly based on surface geophysical, geochemical, and remote sensing data and geological information, are no longer suitable for deep metallogenic prediction due to the large burial depth of deep-seated deposits. Consequently, 3D metallogenic prediction becomes a critical method for delineating deep prospecting target areas. As a world-class giant gold metallogenic province, the Jiaodong Peninsula is at the forefront in China in terms of deep prospecting achievements and exploration depth. Therefore, it has unique conditions for 3D metallogenic prediction and plays an important exemplary role in promoting the development of global deep prospecting. This study briefly introduced the method, bases, and results of the 3D metallogenic prediction in the northwest Jiaodong Peninsula and then established 3D geological models of gold concentration areas in the northwest Jiaodong Peninsula using drilling combined with geophysics. Since gold deposits in the northwest Jiaodong Peninsula are often controlled by faulting in the 3D space, this study proposed a method for predicting deep prospecting target areas based on a stepped metallogenic model and a method for predicting the deep resource potential of gold deposits based on the shallow resources of ore-controlling faults. Multiple characteristic variables were extracted from the 3D geological models of the gold concentration areas, including the buffer zone and dip angle of faults, the changing rate of fault dip angle, and the equidistant distribution of orebodies. Using these characteristic variables, five deep prospecting target areas in the Jiaojia and Sanshandao faults were predicted. Moreover, based on the proven gold resources at an elevation of −2000 m and above, the total gold resources of the Sanshandao, Jiaojia, and Zhaoping ore-controlling faults at an elevation of −5000–−2000 m were predicted to be approximately 3377–6490 t of Au. Therefore, it is believed that the total gold resources in the Jiaodong Peninsula are expected to exceed 10,000 t. These new predicted results suggest that the northwest Jiaodong Peninsula has huge potential for the resources of deep gold deposits, laying the foundation for further deep prospecting. [ABSTRACT FROM AUTHOR]

When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge. And when using the data-driven contribution graph method for fault location, due to the influence of the fault variable, the contribution value of the non-fault variable will become larger, which will cause a tailing effect and misdiagnosis. In this paper, a multiblock kernel principal component algorithm (MBKPCA) is proposed. In this algorithm, the variables of the pitch system operation process are divided into several variable blocks based on the historical fault data and perform faults based on the kernel principal component algorithm for each variable block diagnosis. Taking historical data of an area in North China as an actual calculation example, the proposed block kernel principal component analysis algorithm is compared with the traditional kernel principal component analysis algorithm. The results show that the MBKPCA can effectively reduce the “tailing effect” of the fault variable on the non-fault variable when the contribution graph method is used to identify the fault source, and the method can accurately locate the fault source and achieves higher detection accuracy.

Lagrange polynomials based Chebyshev Pseudo Spectral Time Domain (L‐CPSTD) method is an accurate time domain solver for electromagnetic problems. It utilizes the Lagrange interpolation polynomials to expand electromagnetic fields. This global interpolation, which uses all field values on all of the grid points to calculate the value at a single point, provides spectral accuracy. However, absorbing boundary conditions (ABCs) must be applied for open space problems. Engquist–Majda ABC is an important one due to its simplicity. Characteristic variables (CVs) can be used to implement the ABCs. In this article, for the first time, stability and accuracy of the Engquist–Majda ABC are proved by using the CVs in the L‐CPSTD solution of Maxwell's equations. The theoretical findings are verified by using the matrix eigenvalue method and the reflection coefficient in one and two dimensional open space examples. The numerical result is also validated by an analytical and an FDTD solution of a parallel‐plate waveguide problem. The efficiency of the proposed ABC is clearly shown. [ABSTRACT FROM AUTHOR]

The rapid determination of the water content of vermiculite substrates can promote the efficiency of desert agricultural facilities. The near-infrared spectroscopy protocol developed in this research could be applied to rapidly and quantitatively detect the water content of vermiculite substrates commonly used in desert agricultural facilities. Two different feature extraction algorithms were used to screen characteristic variables. The partial least-squares and multiple linear regression methods were used to establish a relationship model. The multiple linear regression model with feature variables extracted by a successive projection algorithm based on Savitzky–Golay smoothing preprocessing had the best performance, with a prediction-to-deviation ratio of 11.75. The results of this study provide a feasible method for rapidly detecting the moisture content of vermiculite substrates. Except for MSC preprocessing, other preprocessing algorithms improved the prediction accuracy of the model. The combination of derivative and SG smoothing preprocessing gave full play to their data-improving capability, and the modeling performance was significantly enhanced. Continuous projections, algorithm screening, and competitive adaptive resampling were used to optimize the characteristic variables related to the detection of the water content in the vermiculite matrix. The CARS feature extraction algorithm performed better than SPA scveaned for reducing the amount of data. Except for a small amount of preprocessing, the MLR algorithm provided superior predictions to the PLSR algorithm. Among them, under the conditions of SG smoothing pretreatment and SPA feature extraction, the MLR algorithm had the best modeling and prediction effect. [ABSTRACT FROM AUTHOR]

[Introduction] (The SOH(State of Health) evaluation of lithium-ion batteries is difficult to meet the on-board environment requirements of electric vehicles because of the dispersion of evaluation results was caused by the inconsistent characteristics of batteries. [Method] To solve the problem, the corresponding changes of open circuit voltage curve, pulse voltage response and incremental capacity curve of typical energy storage component NCM battery during life cycle test were analyzed. 6 characteristic variables which were closely related to the battery capacity loss were selected, and a comprehensive SOH evaluation method based on fuzzy logic was proposed. In this method, membership function was used to establish the relationship between SOH evaluation sets and variable indicators, and analytic hierarchy process based on correlation coefficient was used to determine the weights of variable indicators that have an impact on the evaluation results. Finally, the validity of the proposed method was verified by four NCM-21700 batteries that completed the life cycle test. [Result] The results show that the method can effectively reduce the dispersion of SOH evaluation, and the average error is not more than 3% as well as the maximum error is no more than 5%. [Conclusion] This work provides some guidance for further study on state of health evaluation of lithium-ion batteries.

Hyperspectral data play an important role in monitoring and protecting wetlands because it not only offers high resolution and wide observation range but also reveal plant information. To explore the hyperspectral characteristics of wetland plants and their applicability in identification, in situ hyperspectral data for 15 species of wetland plants were collected from the Hanshiqiao Wetland Nature Reserve in Beijing, a constructed wetland. Then, first derivative, second derivative, and logarithmic transformation of the reciprocal of the hyperspectral reflectance data were performed using three data conversion methods (principal component analysis, vegetation index, and full-band spectrum) to identify characteristic hyperspectral variables. Four classifiers were compared, namely, support vector machine, decision tree, back-propagation neural network, and random forest. The results demonstrated that: (1) for a comprehensive comparison of the four spectral features data, logarithmic treatment of the reciprocal significantly improved the identification accuracy; (2) for the three data conversion methods for identifying spectral characteristics, the identification accuracy based on the full-band spectrum was better than the other two data forms, and the identification effectiveness of principal component analysis was better than that of the vegetation index; (3) because of differences in plant types and data formats, none of the four classifiers had obvious advantages or disadvantages over the others; and (4) the identification results for all plant statistics further verified the applicability of full-band spectrum data for plant identification. The results showed that the vegetation index and principal component variables reduced the calculation time but did not improve the identification accuracy. Overall, the spectral variables and classifiers examined in this study could meet the needs of wetland plant identification to a certain extent. [ABSTRACT FROM AUTHOR]

Tree peony seed has recently been introduced to produce a high-quality edible oil, rich in the green and organic nutritional ingredients. This study aims to explore the rapid detection for the content of moisture with the near-infrared spectroscopy (NIRS) in oil tree peony seed, and thereby to improve the accuracy of hyper-spectral estimation for the moisture content of peony seed oil. A specific modeling was developed to evaluate the moisture content in oil tree peony seeds, using the advanced hyper spectra technology. The near-infrared spectral reflectance measurements were used to collect the data in the wavelength of 350 to 2 500 nm using the spectrometer (SVC HR-1024i). An oven drying method was selected to obtain the moisture content of seeds. 156 samples were collected in total, two thirds of which were marked as the training set, and one third as the validation set. The constructed model was verified, according to the training set and the validation set. A systematic analysis was performed on the correlation between near-infrared absorption spectra, first derivative spectra, characteristic parameters of moisture absorption, and moisture content. The Single Linear Regression (SLR) models were established to evaluate the moisture content, according to the characteristic wavelength of absorption spectra, characteristic wavelength of first derivative spectra, and characteristic parameters of moisture absorption. Taking 2 characteristic wavelength first derivative spectra and 3 characteristic moisture absorption depth parameters as the input parameters, a BP Neural Network (BPNN) model was built, where the measured moisture content values were set as the output parameters. A Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR) were used to simulate the moisture content, using the same input parameters. The predictive powers of SLR, SMLR and PLSR models were compared with that of the BPNN model. The results showed that: 1) The characteristic wavelength of moisture content absorption spectrum was located at 1410, 1900 and 1990 nm, and that of first derivative spectra was located at 1 150, 1 950 and 2 080 nm. 2) The moisture absorption characteristic parameters with the correlation coefficient greater than 0.9 were AD1930, AD2140 and AD1440. 3) The spectral characteristic variables for DF2080 (R=0.945) and AD2140 (R=-0.956) were significantly related with the moisture content values, and their linear models were achieved optimal for the better estimation models of moisture content. 4) In building BPNN model, the input parameters was set as the selected spectral characteristic variables during the single linear regression model using the hyper-spectral characteristic parameter variables, whereas, the moisture content values as the output parameters. The calibration and validation R² of BPNN model for predicting moisture content were 0.978 and 0.973, the RMSE of 0.220% and 0.242%, the RPD of 6.478 and 5.889, respectively. Compared with other regression models, the BPNN model had the highest calibration and prediction accuracy. The SMLR model based on the selected spectral characteristic variables performed second to BP neural network. Furthermore, the SLR model was the simple method easy to operate. As such, the SLR model can be an optimal selection method under the condition of accurate moisture estimation, indicating that a real-time and high efficient method for the evaluation on the moisture content of oil tree peony seed. The finding can provide a sound theoretical basis to improve the remote sensing inversion accuracy of seed moisture content. [ABSTRACT FROM AUTHOR]

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With its popularisation, electric vehicle (EV) has been part of the important load of smart grid. The analysis on charging demands of EVs is the basis of the study of influences on power grid and charging infrastructures planning. Charging demand calculation is based on the precise analysis on the driving law of users. This study proposes a method based on the stochastic simulation of trip chain to solve the existing problem. First, the concept and characteristic variables (trip start time, driving time, parking duration, driving distance and trip purpose) of trip chain are introduced. Second, the probability distribution models of these variables are established considering their correlative relationships. Third, using Monte Carlo method, the complete trip chains are simulated, and then the spatial–temporal distributions of charging demands are calculated. Finally, based on the National household trip survey data, and according to the proposed method, charging demands in different areas are studied. The results show that the proposed method can accurately simulate the driving law of users and reflect spatial–temporal distribution characteristics of charging demands, and different charging scenarios will lead to different forms of charging demand distribution, which will exert different impacts on power system. [ABSTRACT FROM AUTHOR]

Lagrange polynomials-based Chebyshev pseudospectral time-domain (CPSTD) method uses Lagrange interpolation polynomials as basis functions to expand electromagnetic fields. Using the field values on all of the grids, Lagrange interpolation polynomials assure global calculation of spatial derivatives rather than local calculations. Because of this global approach, the method has spectral accuracy. Moreover, modeling of complex geometries is possible without introducing additional errors since the method is inherently conformal. Generally, the boundary conditions and multidomain patching are applied through characteristic variables (CVs) approach. However, the method suffers from long-time instability because of the boundary condition implementations. In this paper, the reasons for the instability of the method are analyzed and a stable solution for the cavity resonator simulation is proposed and verified. [ABSTRACT FROM PUBLISHER]

Multidomain pseudospectral time-domain (MPSTD) algorithm, previously proposed for a Cartesian coordinates system, is extended to Maxwell's equations in a curvilinear coordinates system and applied to electromagnetic wave problems. A novelty patching technique is derived to effectively implement the boundary conditions at the interface between two adjacent subdomains by using characteristic variables. A perfectly matched layer (PML) in a curvilinear coordinates system is constructed according to a well-posed PML in a Cartesian coordinates system. Numerical results are presented to illustrate the efficiency and accuracy of the method. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2618–2624, 2007; Published online in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mop.22772 [ABSTRACT FROM AUTHOR]

Moisture content is an important index to evaluate the water content in substrate. Near-infrared (NIR) spectroscopy was used for rapid quantitative detection of moisture content of coco-peat substrate. The different spectral pretreatment methods were adopted to pre-process the spectral data. Successive projection algorithm (SPA), elimination of uninformative variables algorithm (UVE) and synergy interval partial least squares algorithm (Si-PLS) were used to screen characteristic variables of coco-peat substrate original spectral data and different pretreatment spectral data. The partial least squares (PLSR) and multiple linear regression (MLR) were used to establish the relationship model between the spectral data and reference measurement value of moisture content. In comparison, the best and simplest spectral prediction model was established when SPA was used to screen the characteristic variables of Savitzky-Golay (S-G) smoothing spectral data and MLR was used to establish the model. And the corresponding correlation coefficient and root mean square error of calibration set were 0.9976 and 1.0989%, respectively; the correlation coefficient and root mean square error of prediction set were 0.9963 and 1.4029%, respectively, and RPD was 11.28. The results of this study provided a feasible method for the rapid detection of moisture content of coco-peat substrate. Unlabelled Image • A new rapid measurement method to detect coco-peat substrate moisture content was presented. • SPA, UVE and Si-PLS effectively simplify moisture content prediction model of coco-peat substrate. • The effects of pre-processing on modeling and screening characteristic variables were studied. • Investigation of prediction performance with whole band and characteristic variables. • Optimal model for coco-peat substrate moisture content detection was established. [ABSTRACT FROM AUTHOR]